Decision Fusion in Space-Time Spreading aided Distributed MIMO WSNs

In this letter, we propose space-time spreading (STS) of local sensor decisions before reporting them over a wireless multiple access channel (MAC), in order to achieve flexible balance between diversity and multiplexing gain as well as eliminate any chance of intrinsic interference inherent in MAC scenarios. Spreading of the sensor decisions using dispersion vectors exploits the benefits of multi-slot decision to improve low-complexity diversity gain and opportunistic throughput. On the other hand, at the receive side of the reporting channel, we formulate and compare optimum and sub-optimum fusion rules for arriving at a reliable conclusion.Simulation results demonstrate gain in performance with STS aided transmission from a minimum of 3 times to a maximum of 6 times over performance without STS.Comment: 5 pages, 5 figure

Wireless industrial monitoring and control networks: the journey so far and the road ahead

While traditional wired communication technologies have played a crucial role in industrial monitoring and control networks over the past few decades, they are increasingly proving to be inadequate to meet the highly dynamic and stringent demands of today’s industrial applications, primarily due to the very rigid nature of wired infrastructures. Wireless technology, however, through its increased pervasiveness, has the potential to revolutionize the industry, not only by mitigating the problems faced by wired solutions, but also by introducing a completely new class of applications. While present day wireless technologies made some preliminary inroads in the monitoring domain, they still have severe limitations especially when real-time, reliable distributed control operations are concerned. This article provides the reader with an overview of existing wireless technologies commonly used in the monitoring and control industry. It highlights the pros and cons of each technology and assesses the degree to which each technology is able to meet the stringent demands of industrial monitoring and control networks. Additionally, it summarizes mechanisms proposed by academia, especially serving critical applications by addressing the real-time and reliability requirements of industrial process automation. The article also describes certain key research problems from the physical layer communication for sensor networks and the wireless networking perspective that have yet to be addressed to allow the successful use of wireless technologies in industrial monitoring and control networks

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Increasing Network Lifetime in an Energy-Constrained Wireless Sensor Network

International audienceEnergy in Wireless Sensor Networks is a scarce resource, therefore an energy-efficient management is required to increase the network lifetime. In this paper, we study the problem of optimal power allocation, taking into account the estimation of total signal-to-noise ratio (SNR) at the Fusion Center (FC). We consider that nodes transmit their data to the Fusion Center over quasi-static Rayleigh fading channels (QSRC). In order to analyze our approach, we will investigate first the orthogonal channels, and secondly the non-orthogonal ones introducing a virtual MISO in the communication. We consider in both cases that the nodes have Channel State Information (CSI). Simulations that have been conducted using these two channel configurations show that, thanks to our new algorithm, the network lifetime is extended by an average that can reach 82,80% compared to the network lifetime in the other methods